Identification of signaling pathways related to drug efficacy in hepatocellular carcinoma via integration of phosphoproteomic, genomic and clinical data

Hepatocellular Carcinoma (HCC) is one of the leading causes of death worldwide, with only a handful of treatments effective in unresectable HCC. Most of the clinical trials for HCC using new generation interventions (drug-targeted therapies) have poor efficacy whereas just a few of them show some promising clinical outcomes [1]. This is amongst the first studies where the mode of action of some of the compounds extensively used in clinical trials is interrogated on the phosphoproteomic level, in an attempt to build predictive models for clinical efficacy. Signaling data are combined with previously published gene expression and clinical data within a consistent framework that identifies drug effects on the phosphoproteomic level and translates them to the gene expression level. The interrogated drugs are then correlated with genes differentially expressed in normal versus tumor tissue, and genes predictive of patient survival. Although the number of clinical trial results considered is small, our approach shows potential for discerning signaling activities that may help predict drug efficacy for HCC.National Institutes of Health (U.S.) (Grant U54-CA119267)National Institutes of Health (U.S.) (Grant R01-CA96504

Percolation approach to quark gluon plasma in high energy pp collisions

We apply continuum percolation to proton-proton collisions and look for the possible threshold to phase transition from confined nuclear matter to quark gluon plasma. Making the assumption that J/Psi suppression is a good signal to the transition, we discuss this phenomenon for pp collisions, in the framework of a dual model with strings.Comment: 8 pages, 3 figure

Studying minijets via the pTp_T dependence of two-particle correlation in azimuthal angle ϕ\phi

Following my previous proposal that two-particle correlation functions can be used to resolve the minijet contribution to particle production in minimum biased events of high energy hadronic interactions, I study the $p_T$ and energy dependence of the correlation. Using HIJING Monte Carlo model, it is found that the correlation $c(\phi_1,\phi_2)$ in azimuthal angle $\phi$ between two particles with $p_T>p_T^{cut}$ resembles much like two back-to-back jets as $p_T^{cut}$ increases at high colliding energies due to minijet production. It is shown that $c(0,0)-c(0,\pi)$, which is related to the relative fraction of particles from minijets, increases with energy. The background of the correlation for fixed $p_T^{cut}$ also grows with energy due to the increase of multiple minijet production. Application of this analysis to the study of jet quenching in ultrarelativistic heavy ion collisions is also discussed.Comment: 11 pages Latex text and 8 ps figures, LBL-3349

Combined logical and data-driven models for linking signalling pathways to cellular response

Background Signalling pathways are the cornerstone on understanding cell function and predicting cell behavior. Recently, logical models of canonical pathways have been optimised with high-throughput phosphoproteomic data to construct cell-type specific pathways. However, less is known on how signalling pathways can be linked to a cellular response such as cell growth, death, cytokine secretion, or transcriptional activity. Results In this work, we measure the signalling activity (phosphorylation levels) and phenotypic behavior (cytokine secretion) of normal and cancer hepatocytes treated with a combination of cytokines and inhibitors. Using the two datasets, we construct "extended" pathways that integrate intracellular activity with cellular responses using a hybrid logical/data-driven computational approach. Boolean logic is used whenever a priori knowledge is accessible (i.e., construction of canonical pathways), whereas a data-driven approach is used for linking cellular behavior to signalling activity via non-canonical edges. The extended pathway is subsequently optimised to fit signalling and behavioural data using an Integer Linear Programming formulation. As a result, we are able to construct maps of primary and transformed hepatocytes downstream of 7 receptors that are capable of explaining the secretion of 22 cytokines. Conclusions We developed a method for constructing extended pathways that start at the receptor level and via a complex intracellular signalling pathway identify those mechanisms that drive cellular behaviour. Our results constitute a proof-of-principle for construction of "extended pathways" that are capable of linking pathway activity to diverse responses such as growth, death, differentiation, gene expression, or cytokine secretion.Marie Curie International Reintegration Grants (MIRG-14-CT-2007-046531)Vertex Pharmaceuticals IncorporatedBundesministerium für Wissenschaft und Forschung (HepatoSys)Massachusetts Institute of Technology (Rockwell International Career Development Professorship)Bundesministerium für Wissenschaft und Forschung (HepatoSys 0313081D

Weighted norm inequalities, off-diagonal estimates and elliptic operators. Part IV: Riesz transforms on manifolds and weights

This is the fourth article of our series. Here, we study weighted norm inequalities for the Riesz transform of the Laplace-Beltrami operator on Riemannian manifolds and of subelliptic sum of squares on Lie groups, under the doubling volume property and Gaussian upper bounds.Comment: 12 pages. Fourth of 4 papers. Important revision: improvement of main result by eliminating use of Poincar\'e inequalities replaced by the weaker Gaussian keat kernel bound

Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury

Identification of signaling pathways that are functional in a specific biological context is a major challenge in systems biology, and could be instrumental to the study of complex diseases and various aspects of drug discovery. Recent approaches have attempted to combine gene expression data with prior knowledge of protein connectivity in the form of a PPI network, and employ computational methods to identify subsets of the protein–protein-interaction (PPI) network that are functional, based on the data at hand. However, the use of undirected networks limits the mechanistic insight that can be drawn, since it does not allow for following mechanistically signal transduction from one node to the next. To address this important issue, we used a directed, signaling network as a scaffold to represent protein connectivity, and implemented an Integer Linear Programming (ILP) formulation to model the rules of signal transduction from one node to the next in the network. We then optimized the structure of the network to best fit the gene expression data at hand. We illustrated the utility of ILP modeling with a case study of drug induced lung injury. We identified the modes of action of 200 lung toxic drugs based on their gene expression profiles and, subsequently, merged the drug specific pathways to construct a signaling network that captured the mechanisms underlying Drug Induced Lung Disease (DILD). We further demonstrated the predictive power and biological relevance of the DILD network by applying it to identify drugs with relevant pharmacological mechanisms for treating lung injury.Institute for Collaborative Biotechnologies (Grant W911NF-09-0001

Multi-boson effects and the normalization of the two-pion correlation function

The two-pion correlation function can be defined as a ratio of either the measured momentum distributions or the normalized momentum space probabilities. We show that the first alternative avoids certain ambiguities since then the normalization of the two-pion correlator contains important information on the multiplicity distribution of the event ensemble which is lost in the second alternative. We illustrate this explicitly for specific classes of event ensembles.Comment: 6 pages, three figures,submit to PR